SIENTIAPDE-1253: Refactor training workflow and activities to raise exceptions on failure

This commit refactors the training workflow and associated activities to raise exceptions on failure instead of returning success/failure dictionaries. This allows the Temporal workflow to handle errors more effectively and ensures that the workflow stops when a critical error occurs.

Key changes:

- The train_model workflow is introduced to orchestrate the entire training process, including parameter validation, data download, model training, and model saving.
- The validate_train_params activity is added to validate and convert training parameters.
- The train_model and save_model activities are updated to raise exceptions on failure.
- The ExperimentStatus enum is updated to include a new status for orchestrator validation errors.
- The tests are updated to reflect the new exception-based error handling.
- The activities now return the TrainModelResult directly instead of a dictionary.

model_manager/utils/models/experiment_status.py

@@ -23,6 +23,7 @@ class ExperimentStatus(str, Enum):
         FILE_DELETE_ERROR: Cleanup failed due to file system or MinIO errors.
     """
 
+    ORCHESTRATOR_VALIDATION_ERROR = 'ORCHESTRATOR_VALIDATION_ERROR'
     MAGE_WAITING_PROC = 'MAGE_WAITING_PROC'
     TRAINING_SUCCESS = 'TRAINING_SUCCESS'
     TRAINING_ERROR = 'TRAINING_ERROR'